Location: Microbial and Chemical Food Safety
2021 Annual Report
Objectives
Mathematical models that predict behavior of pathogens in food can be used to verify critical control points in Hazard Analysis and Critical Control Point (HACCP) programs. For example, they can be used to assess whether or not a process deviation results in a one log cycle increase of Clostridium perfringens during cooling of a cooked meat product during commercial processing. Models that predict behavior of pathogens can be integrated with data for pathogen contamination to predict dynamic changes in pathogen prevalence and number in food across unit operations of a production chain. Predictions of consumer exposure can then be used in a dose-response model to form a process risk model that predicts consumer exposure and response to pathogens in food produced by specific scenarios. Process risk models have great potential to improve food safety and public health by providing a better assessment of food safety and identification of risk factors. In the past, we have developed predictive models and process risk models that have proven highly useful in providing regulatory agencies and the food industry with an objective means of assessing food safety and identifying risk factors. The goal of the proposed research is to elevate that successful effort to the next level of sophistication by considering additional variables and developing new and improved models and more effectively transferring this new research to the food industry by providing updated and improved versions of our software products: the Predictive Microbiology Information Portal, ComBase, and the Pathogen Modeling Program.
1: Develop and validate predictive models for behavior of stressed and unstressed pathogens in food with added antimicrobials. This includes development of validated dynamic models for spores and vegetative foodborne pathogens for evaluating heating and cooling process deviations.
2: Develop and validate process risk models for higher risk pathogen and food combinations.
3: Expand and maintain the ARS-Pathogen Modeling Program and Predictive Microbiology Information Portal. Continue to support the development and utilization of ComBase with our associated partners the Institute of Food Research (IFR) and the University of Tasmania (UTas) as an international data resource.
Approach
Effects and interactions of time, temperature, pH, acidulant, water activity, humectant, or preservatives (phosphates, organic acid salts, and nitrite) in meat and poultry products, as well as in rice, beans, and pasta will be assessed to collect kinetic data for pathogens (Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, Clostridium perfringens and Bacillus cereus). Kinetic data will be modeled using primary and secondary models. Predictive models performance will be evaluated using the acceptable prediction zone method. Once validated and published, predictive models will be incorporated into the Pathogen Modeling Program and data will be archived in ComBase.
Kinetic data for development of predictive microbiology models for survival and growth of pathogens (Salmonella, E. coli O157:H7, Campylobacter jejuni, and Listeria monocytogenes) on higher risk food (tomatoes, lettuce, raw milk, and crab meat) will be obtained in inoculated pack studies. Pathogens will be enumerated on higher risk food during storage trials using an automated miniature most probable number method. Kinetic data will be modeled using neural network modeling methods and models will be validated against independent data using the acceptable prediction zone method. Whole sample enrichment real time polymerase chain reaction (WSE-qPCR) will be used to obtain data for prevalence, number, and types of pathogens on higher risk food. Predictive microbiology models and contamination data obtained by WSE-qPCR will be integrated to form process risk models that predict consumer exposure and response to pathogens on higher risk food produced by different scenarios.
All new models will be added to both versions of the Pathogen Modeling Program. A link to ARS, Poultry Food Assess Risk Models website will be provided in the portal. Combase will be made compatible with the PMP.
Progress Report
This is the final report for Project 8072-42000-079-00D, which ended January 10, 2021. New NP108 OSQR approved project 8072-42000-083-00D, entitled “Development and Validation of Predictive Models and Pathogen Modeling Programs; and Data Acquisition for International Microbial Databases,” has been established.
Under Objective 1, predictive models were developed to estimate the extent of Bacillus cereus and Clostridium perfringens growth from spores during cooling of cooked meat and cooked rice, beans, and pasta. The growth data/predictive model on the safe cooling rate enables the food industry and regulatory agencies with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning. The models aid in the disposition of products subject to cooling deviations and assist in designing ‘Hazard Analysis Critical Control Point’ program, setting critical control limits, and in evaluating the relative severity of problems caused by process deviations. Further, these models are used to estimate the expected effectiveness of corrective actions due to deviations from a critical limit.
Under Objective 1, predictive thermal death time models for Escherichia coli O157:H7, Listeria monocytogenes, Salmonella spp., Bacillus cereus spores, and Clostridium perfringens vegetative cells in meat/poultry products and/or rice were developed to estimate the reduced heat treatment that may be employed for the production of safe meat products with extended shelf life. These predictive models help the food industry to obtain rapidly accurate estimates of pathogen behavior in foods, allow food processors to formulate foods to include acknowledged intrinsic barriers, assess the microbial risk of a particular food and design thermal processes that ensure safety against pathogens in ready-to-eat foods with extended shelf life while minimizing quality losses.
Under Objective 2, data was collected, and models were developed for Salmonella: 1) contamination of ground turkey, chicken parts, chicken liver, and chicken gizzard; 2) growth in laboratory broth, chicken skin, ground chicken, ground turkey, chicken liver, chicken gizzard, tomato, lettuce, and cucumber; and 3) death in ground chicken. Data were collected, and a model was developed for the death of Campylobacter in milk and beef. The data and models help find unsafe food. The impact is less illness from food.
Under Objective 3, this project continues to expand the USDA-ARS Pathogen Modeling (computer) Program (PMP) and the Predictive Microbiology Information Portal (PMIP) with the newly developed models. The complex underlying mathematics of the predictive models were transformed into easy-to-use interfaces that can be successfully used by food microbiologists, regulatory staff members, and industry professionals to explore the predictions of these models on scenarios relevant to food processing operations. Since small and very small food processors generally lack food safety resources, the models are particularly helpful to these producers to improve the food safety of their products. Fifteen new models were added to the online version of the PMP. In addition, one of the existing models was removed from the desktop version of the PMP, and Version 8 was released after 13 years. Fifty CDs containing the installation package as a backup for when the website is unavailable to run models or download the installation package were sent to the USDA Food Safety and Inspection Service (FSIS).
Under Objective 3, a new search feature has been added to the ComBase Browser; each record now indicates the date that the record was added to ComBase; an improved and simpler data donation template, plus instructional videos, have been added to the Data Submission page; enhanced messaging on website to promote data donations; each data record now indicates the number of times it has been viewed and downloaded; a YouTube channel and tutorials are now available; a private data section with ComBase is available to embargo data until a publication has been released; the ComBase Predictor was changed to ‘Broth Models’ in the menu, so that it better aligns with the separate suite of ‘Food Models’; a new feature allowing ComBase data to be added (over-laid) on ComBase Predictor graphs; updates to Perfringens Predictor and inactivation models per USDA-FSIS requests (hyperlinks to FSIS documents, increasing time-temp input capacity to ~500 data points, specific directions about how to measure core temperature, allowing a maximum 10°F jump in cooling temp for Perfringens Predictor); displayed all three kinetic parameters—lag, growth rate, MPD—for ComBase Predictor growth model outputs; added a reset button for model default lag time; integrated API feature to link model predictions to 3rd party software. ComBase assists users in predicting and improving the microbiological safety of foods and assessing microbiological risk in foods. Thus, ComBase saves the food industry millions of dollars a year by reducing the need for costly microbiological tests as well as helping to prevent recalls and foodborne illness.
Accomplishments
1. Proper means for cooling of cooked foods. Heat-resistant spores of foodborne pathogens can survive the time and temperature used to cook meat and poultry products in food-service operations. The surviving heat-activated spores pose a public health risk due to their potential for subsequent germination, outgrowth, and multiplication in cooked foods, especially when chilling rate and extent is insufficient. ARS scientists at Wyndmoor, Pennsylvania, assessed the ability of Clostridium perfringens and Clostridium botulinum spores to germinate and grow in cooked pork and chicken, at temperatures applicable to cooling of cooked products. The growth data and predictive models developed on the safe cooling rate will ensure that cooked products remain pathogen-free and safe for human consumption. The retail food industry would need fewer challenge studies to validate the safety of their products.
2. ComBase, an international microbial modeling database. An ARS scientist in Wyndmoor, Pennsylvania, manages ComBase. It is a global collaboration that is growing in size, relevance, and impact. The food industry, government, and international scientists use data in ComBase to develop models that predict food safety and reduce illness from food. There are 83,160 registered users of ComBase. In the past year, there were 69,936 user sessions. The top five countries using ComBase are: 1) Spain; 2) the United States; 3) Italy; 4) the United Kingdom; and 5) Canada. This global collaboration reduces the cost of food safety programs by providing open access data for models that reduce food testing.
Review Publications
Oscar, T.P. 2020. A multiple therapy hypothesis for treatment of COVID-19 patients. Medical Hypotheses. 145. https://doi.org/10.1016/j.mehy.2020.110353.
